Classification of pornographic content on Twitter using support vector machine and Naive Bayes

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)

Abstract

The Internet has many benefits, some of them are to gain knowledge and gain the latest information. The internet can be used by anyone and can contain any information, including negative content such as pornographic content, radicalism, racial intolerance, violence, fraud, gambling, security and drugs. Those contents cause the number of children victims of pornography on social media increasing every year. Based on that, it needs a system that detects pornographic content on social media. This study aims to determine the best model to detect the pornographic content. Model selection is determined based on unigram and bigram features, classification algorithm, k-fold cross validation. The classification algorithm used is Support Vector Machine and Naive Bayes. The highest F1-score is yielded by the model with combination of Support Vector Machine, most common words, and combination of unigram and bigram, which returns F1-Score value of 91.14%.

Original languageEnglish
Title of host publication2018 4th International Conference on Computer and Technology Applications, ICCTA 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages156-160
Number of pages5
ISBN (Electronic)9781538669952
DOIs
Publication statusPublished - 27 Jun 2018
Event4th International Conference on Computer and Technology Applications, ICCTA 2018 - Istanbul, Turkey
Duration: 3 May 20185 May 2018

Publication series

Name2018 4th International Conference on Computer and Technology Applications, ICCTA 2018

Conference

Conference4th International Conference on Computer and Technology Applications, ICCTA 2018
Country/TerritoryTurkey
CityIstanbul
Period3/05/185/05/18

Keywords

  • classification
  • K-fold cross validation
  • naive bayes
  • pornography
  • social media
  • support vector machine
  • text mining

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